<no title>

	time	generation biomass	generation fossil brown coal/lignite	generation fossil gas	generation fossil hard coal	generation fossil oil	...	generation solar	generation waste	generation wind onshore	forecast solar day ahead	forecast wind onshore day ahead	total load forecast	total load actual	price day ahead	price actual
0	2015-01-01 00:00:00+01:00	447.0	329.0	4844.0	4821.0	162.0	...	49.0	196.0	6378.0	17.0	6436.0	26118.0	25385.0	50.10	65.41
1	2015-01-01 01:00:00+01:00	449.0	328.0	5196.0	4755.0	158.0	...	50.0	195.0	5890.0	16.0	5856.0	24934.0	24382.0	48.10	64.92
2	2015-01-01 02:00:00+01:00	448.0	323.0	4857.0	4581.0	157.0	...	50.0	196.0	5461.0	8.0	5454.0	23515.0	22734.0	47.33	64.48
3	2015-01-01 03:00:00+01:00	438.0	254.0	4314.0	4131.0	160.0	...	50.0	191.0	5238.0	2.0	5151.0	22642.0	21286.0	42.27	59.32
4	2015-01-01 04:00:00+01:00	428.0	187.0	4130.0	3840.0	156.0	...	42.0	189.0	4935.0	9.0	4861.0	21785.0	20264.0	38.41	56.04

5 rows × 26 columns

li = range(13)
weather_df = pd.read_csv('weather_features.csv', usecols=li).drop('city_name', axis=1)
weather_df = weather_df.groupby('time').mean()
weather_df.head()

	temp	temp_min	temp_max	pressure	humidity	wind_speed	wind_deg	rain_1h	rain_3h	snow_3h	clouds_all
time
2015-01-01 00:00:00+01:00	272.491463	272.491463	272.491463	1016.4	82.4	2.0	135.2	0.0	0.0	0.0	0.0
2015-01-01 01:00:00+01:00	272.512700	272.512700	272.512700	1016.2	82.4	2.0	135.8	0.0	0.0	0.0	0.0
2015-01-01 02:00:00+01:00	272.099137	272.099137	272.099137	1016.8	82.0	2.4	119.0	0.0	0.0	0.0	0.0
2015-01-01 03:00:00+01:00	272.089469	272.089469	272.089469	1016.6	82.0	2.4	119.2	0.0	0.0	0.0	0.0
2015-01-01 04:00:00+01:00	272.145900	272.145900	272.145900	1016.6	82.0	2.4	118.4	0.0	0.0	0.0	0.0

df = weather_df.merge(energy_df, on='time')[:8760]
times = []
for time in df['time']:
    good, bad = time.split('+')
    times.append(good)
df['time']= times
df.head()

	time	temp	temp_min	temp_max	pressure	humidity	wind_speed	wind_deg	...	generation solar	generation waste	generation wind onshore	forecast solar day ahead	forecast wind onshore day ahead	total load forecast	total load actual	price day ahead	price actual
0	2015-01-01 00:00:00	272.491463	272.491463	272.491463	1016.4	82.4	2.0	135.2	...	49.0	196.0	6378.0	17.0	6436.0	26118.0	25385.0	50.10	65.41
1	2015-01-01 01:00:00	272.512700	272.512700	272.512700	1016.2	82.4	2.0	135.8	...	50.0	195.0	5890.0	16.0	5856.0	24934.0	24382.0	48.10	64.92
2	2015-01-01 02:00:00	272.099137	272.099137	272.099137	1016.8	82.0	2.4	119.0	...	50.0	196.0	5461.0	8.0	5454.0	23515.0	22734.0	47.33	64.48
3	2015-01-01 03:00:00	272.089469	272.089469	272.089469	1016.6	82.0	2.4	119.2	...	50.0	191.0	5238.0	2.0	5151.0	22642.0	21286.0	42.27	59.32
4	2015-01-01 04:00:00	272.145900	272.145900	272.145900	1016.6	82.0	2.4	118.4	...	42.0	189.0	4935.0	9.0	4861.0	21785.0	20264.0	38.41	56.04

5 rows × 37 columns

sum_biomass = df['generation biomass'].sum()
sum_coal = df['generation fossil brown coal/lignite'].sum()
sum_gas = df['generation fossil gas'].sum()
sum_hard_coal = df['generation fossil hard coal'].sum()
sum_oil = df['generation fossil oil'].sum()
sum_hydro = df['generation hydro pumped storage consumption'].sum() + df['generation hydro run-of-river and poundage'].sum() + df['generation hydro water reservoir'].sum()
sum_nuclear = df['generation nuclear'].sum()
sum_other = df['generation other'].sum() + df['generation other renewable'].sum() + df['generation waste'].sum() + df['generation fossil oil'].sum() + df['generation fossil brown coal/lignite'].sum() + df['generation biomass'].sum()
sum_solar = df['generation solar'].sum()
sum_waste = df['generation waste'].sum()
sum_wind = df['generation wind onshore'].sum()


labels = ['Gas', 'Hard coal', 'Hydro', 'Nuclear', 'Other', 'Solar', 'Wind']
values = [sum_gas, sum_hard_coal, sum_hydro, sum_nuclear, sum_other, sum_solar, sum_wind]
layout = go.Layout(
    height=600,
    title='Sold houses in the Netherlands, by type, in 2017',
    showlegend=False,
    hovermode=False,

)

fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.8, textinfo='label + percent', marker=dict(colors=px.colors.qualitative.T10), textposition='outside')], layout=layout)

fig.show()

df['date'] = pd.to_datetime(df['time'])

fig = px.scatter(df, x=df['wind_speed'], y=df['generation wind onshore'], color=df['price actual'], opacity=1, animation_frame=df['date'].dt.month_name())
fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 3000

fig.show()

values_wind = df['generation wind onshore']
total = df['total load actual']
values_coal = df['generation fossil hard coal']
values_nuclear = df['generation nuclear']

wind_percentages = []
coal_percentages = []
nuclear_percentages = []
for i in range(len(values_coal)):
    if 0<values_wind[i]/total[i]<1:
        wind_percentages.append(values_wind[i]/total[i])
    else:
        wind_percentages.append(0)
    if 0<values_coal[i]/total[i]<1:
        coal_percentages.append(values_coal[i]/total[i])
    else:
        coal_percentages.append(0)
    if 0<values_nuclear[i]/total[i]<1:
        nuclear_percentages.append(values_nuclear[i]/total[i])
    else:
        nuclear_percentages.append(0)
df_2= pd.DataFrame(data =[wind_percentages, coal_percentages, nuclear_percentages]).T
df_2.columns=['wind', 'coal', 'nuclear']

smoothed_values_wind = df_2['wind'].rolling(365, min_periods=1, center=True).mean()
smoothed_values_coal = df_2['coal'].rolling(365, min_periods=1, center=True).mean()
smoothed_values_nuclear = df_2['nuclear'].rolling(365, min_periods=1, center=True).mean()

fig = go.Figure()
fig.add_trace(go.Scatter(x=df['time'], y=smoothed_values_wind, name='Percentage wind energy', line=dict(color='blue')))
fig.add_trace(go.Scatter(x=df['time'], y=smoothed_values_coal, name='Percentage coal energy', line=dict(color='orange')))

fig.update_layout(title='Percentage contribution of total energy used', hovermode=False)
fig.show()
# fig = px.line(x=df['time'], y=smoothed_values, title='Life expectancy in Canada')
# fig.show()

color_wind = 'blue'
color_nuclear = 'orange'
fig = go.Figure()
fig.add_trace(go.Box(y=df['generation wind onshore'], name='Wind energy generated', marker_color=color_wind))
fig.add_trace(go.Box(y=df['generation nuclear']*df['generation wind onshore'].mean()/df['generation nuclear'].mean(), name='Nuclear energy generated', marker_color=color_nuclear))
fig.update_layout(hovermode=False, showlegend=False, title='Distribution of wind and nuclear energy scaled to the same average')

color_wind = 'blue'
color_nuclear = 'orange'
color_hydro = 'pink'
fig = go.Figure()
fig.add_trace(go.Box(y=df['generation wind onshore'], name='Wind energy generated', marker_color=color_wind))
fig.add_trace(go.Box(y=df['generation solar']*df['generation wind onshore'].mean()/df['generation solar'].mean(), name='Solar energy generated', marker_color=color_nuclear))
fig.add_trace(go.Box(y= df['generation hydro water reservoir'] * df['generation wind onshore'].mean() / df['generation hydro water reservoir'].mean(), name='Hydro energy generated', marker_color=color_hydro))
fig.update_layout(hovermode=False, showlegend=False, title='Distribution of wind and solar energy scaled to the same average')
fig.show()

smoothed_values_wind = df['generation wind onshore'].rolling(365, min_periods=1, center=True).mean()
smoothed_values_solar = df['generation solar'].rolling(365, min_periods=1, center=True).mean()
smoothed_values_hydro = df['generation hydro water reservoir'].rolling(365, min_periods=1, center=True).mean()

fig = go.Figure()
fig.add_trace(go.Scatter(x=df['time'], y=smoothed_values_wind, name='Percentage wind energy', line=dict(color='blue')))
fig.add_trace(go.Scatter(x=df['time'], y=smoothed_values_solar, name='Percentage solar energy', line=dict(color='orange')))
fig.add_trace(go.Scatter(x=df['time'], y=smoothed_values_hydro, name='Percentage hydro energy', line=dict(color='pink')))


fig.update_layout(title='Percentage contribution of total energy used', hovermode=False)
fig.show()

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